Name
Abstract | ||
|---|---|---|
Mining frequent tree patterns has many practical applications in areas such as XML document mining, web mining, bioinformatics, network routing and so on. Most of the previous works used an apriori-based approach for candidate generation and frequency counting in their algorithms. In these approaches the state space grows exponentially since many unreal candidates are generated, especially when there are lots of large patterns among the data. To tackle these problems, we propose TDU, a Top-Down approach for mining all maximal, labeled, Unordered, and embedded subtrees from a collection of tree-structured data. We would evaluate the effectiveness of the TDU algorithm in comparison to the previous works. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/CIDM.2007.368907 | 2007 IEEE SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND DATA MINING, VOLS 1 AND 2 |
Keywords | Field | DocType |
tree structure,top down,data mining,network routing,tree data structures,xml document,web mining,state space | Data mining,Data stream mining,Concept mining,Web mining,XML,Computer science,Molecule mining,Tree (data structure),Decision tree learning,Search tree | Conference |
Citations | PageRank | References |
5 | 0.42 | 11 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mostafa Haghir Chehreghani | 1 | 50 | 8.46 |
| Masoud Rahgozar | 2 | 72 | 8.77 |
| Caro Lucas | 3 | 1501 | 103.34 |
| Morteza Haghir Chehreghani | 4 | 110 | 16.07 |